Join the premier machine learning Livestream event where to meet the who’s who and keep up on the latest techniques, making it the leading machine learning event that excites and unites.
Machine Learning Week Virtual North America brings together six co-located events: PAW Business, PAW Financial, PAW Industry 4.0, PAW Healthcare, PAW Climate and Deep Learning World’ plus a range of training workshop options.
Since 2009, it has been the leading event covering advanced analytics and machine learning. It is the only conference of its kind, with sessions and content reaching across business applications, across industries and across vendors of solutions and software.
The Six Conferences at Machine Learning Week:
Predictive Analytics World for Business delivers concrete examples of deployed machine learning. Hear from the horse’s mouth precisely how Fortune 500 analytics competitors and other top practitioners deploy machine learning, and the kind of business results they achieve.
Predictive Analytics World for Financial Services is the leading event covering the deployment of machine learning by banks, insurance companies, credit card companies, investment firms, and other financial institutions.
Predictive Analytics World for Healthcare’s program focuses on how data science and machine learning are employed at leading Healthcare and Clinical enterprises, resulting in improved outcomes, lower costs, and higher patient satisfaction.
Predictive Analytics World for Industry 4.0 is the leading vendor-neutral conference for machine learning for smart manufacturing and IoT.
Predictive Analytics World Climate Tech explores how companies apply machine learning to problems such as smart electrical grids, supply chain optimization, building energy efficiency, industrial control, precision agriculture, climate risk assessment, weather forecasting, ecosystem monitoring, and disaster response.
Deep Learning World is the premier conference covering the commercial deployment of deep learning. The event’s mission is to foster breakthroughs in the value-driven operationalization of established deep learning methods.
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